The blog discusses machine learning concepts and demonstrates building an application that allows users to ask questions from a PDF on their local machine for free. It covers the definitions of models, embeddings, and vector databases, explaining their roles in AI and ML. Embedding models are tools that simplify complex data into numerical forms, while vector databases store high-dimensional vectors efficiently. The blog also introduces Large Language Models (LLMs), which generate human-like text based on learned patterns.
The workflow involves converting PDFs to text, creating embeddings, querying vector databases, and using LLMs for output. Prerequisites include installing Python, downloading the ollama platform for running LLMs locally, and choosing suitable models from OpenAI and Huggingface. Quick start commands like “ollama run llama3” and “ollama pull nomic-embed-text” are provided for running models and embedding models. Testing installation is demonstrated using a sample code snippet.
Overall, the blog aims to familiarize readers with these concepts and provide a practical example of working with models, embeddings, and vector databases on a local machine. It emphasizes the importance of understanding these components in the context of AI and ML applications.
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Source link: https://shashwat-creator.medium.com/developer-guide-to-start-with-ai-ml-pdf-to-chat-bot-b2bb168faf07?source=rss——chatgpt-5